NYU Data Science newsletter – October 19, 2015

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Cyber or information-environment security is often in the news, yet most of our focus has been on theft of intellectual property, denial of service attacks, and assaults on personal privacy. Far less attention has been paid to the ways social media have facilitated a level of propaganda and falsehoods, which is far more pernicious than anything previously experienced. The continuous assault on objective, truthful information threatens to undermine democratic institutions, including a free press.

The unfortunate truth about data science professionals is that they spend a shockingly small amount of time actually exploring data. Instead, they are stuck devoting significant amounts of time wrangling data and pouring resources into the tedious act of prepping and managing it.

While Hadoop excels at turning massive amounts of data into valuable insights, it’s also a notorious culprit for sucking up resources. In fact, these hurdles are serious bottlenecks to big data success, with research firm Gartner predicting that through 2018, 70 percent of Hadoop deployments will not meet cost savings and revenue generation objectives due to skills and integration challenges.

Our World In Data covers a wide range of topics and visualizes the empirical evidence of how living standards changed over the last decades, centuries, and millennia.

When hiring data scientists, people tend to focus primarily on technical qualifications. It’s hard to find candidates who have the right mix of computational and statistical skills. But what’s even harder is finding people who have those skills and are good at communicating the story behind the data.

… For the past decade, information scientists have been advocating that we involve people on a more meaningful level in the information-seeking process. In particular, Gary Marchionini, dean of Information and Library Science at the University of North Carolina at Chapel Hill, has advocated for “human-computer information retrieval” systems that increase user responsibility and control.

This sounds like a great idea. But how do we do it?

… universities across the country are picking up on Data Science’s growing importance. Nowadays, businesses – regardless of their industry – are looking to accumulate, analyze and apply data to drive success in their space. To do that, it’s becoming key for employers to get their hands on Data Science talent. Select schools are stepping up and designing programs catered to developing students to thrive in this field.

There are Data Science – or something similarly named like Business Analytics or Data Mining – programs popping up in every U.S. state. In Massachusetts, schools like UMass and Worcester Polytechnic Institute (WPI), are among some of the first Data Science program pioneers.

In modern campaigns, buzzwords like “microtargeting” and “big data” are often bandied about as essential to victory. These terms refer to the practice of analyzing (or “microtargeting”) millions of voter registration records (“big data”) to predict who will vote and for whom.

If you’ve ever gotten a message from a campaign, there’s a good chance you’ve been microtargeted. Serious campaigns use microtargeting to persuade voters through mailings, phone calls, knocking on doors, and — in our increasingly connected world — social media.

But the big data that fuels such efforts comes at a big price, which can create a serious barrier to entry for candidates and groups seeking to participate in elections — that is, if they are allowed to buy the data at all.

It has been five years since the US Congress enacted the landmark Dodd–Frank Wall Street Reform and Consumer Protection Act; and despite the fact that about 20% of the Act has yet to be implemented, several legislative initiatives are now attempting to soften or roll back key provisions. This pattern of regulatory action and reaction is not new. The financial excesses of one period often lead to asset bubbles that burst, ushering in a new period of much greater regulation. This, in turn, is systematically weakened over time as markets recover and we forget the reasons why we imposed such stringent regulations in the first place. Even before Dodd–Frank, the financial industry was among the most highly regulated of industries in the world. However, the many layers of regulation and multiple regulatory agencies were insufficient to prevent financial crisis. Why?

… The link between changes in the microbiome and disease is an emerging area of research, and the possibility of targeting the microbiome to prevent or treat a range of disorders holds promise for the drug discovery field.

The development of therapeutics will require abundant data and deeper knowledge of microbiome diversity, normal versus abnormal variation, and host–microbiome interaction. Fortunately, data in abundance and knowledge in depth are becoming available through metagenomics, the study and profiling of genomes from microbial communities that constitute the microbiome. Metagenomics, then, promises to advance drug discovery.

In 2008, Google launched “Flu Trends.” It was an attempt to track the spread of influenza based on “aggregated search queries.” According to a 2015 blog post, Google Flu Trends will no longer continue with its own website. Rather, it will be providing data directly to infectious disease researchers at institutions like Columbia University’s Mailman School of Public Health. One of those researchers is Jeffrey Shaman. He lead a team that won the Centers for Disease Control and Prevention “Predict the Influenza Season Challenge” last year by creating a flu forecast. Much like a weather forecast that predicts a 70 percent chance of rain, for example, Shaman’s models sought to predict a 70 percent chance of influenza.

Meanwhile, Rumi Chunara, who works across fields like public health and computer science at New York University, is also looking to map the spread of the flu, but at a more granular level. As a part of the GoViral study, she collects fluid specimens—such as saliva and nasal swabs—to track who has the flu, and who in the surrounding community is at risk for getting it. Chunara and Shaman join Ira to talk about how we’re getting better at predicting the flu and how our improvement can help us combat it. [audio, 11:44]

… “Our conclusion is that breaks in repetition allow the visual system some time to rest and allow autistic individuals to learn efficiently and to then generalize,” says David Heeger, professor of psychology and neural science at New York University. “Repeated stimulation leads to sensory adaptation which interferes with learning and makes learning specific to the adapted conditions. Without adaptation, learning is more efficient and can be generalized.”

The research team believes that the findings, published in the journal Nature Neuroscience, have important implications for educating individuals with autism.